Science Policy Around the Web – February 21, 2017

Researchers are modeling obesity from a public health perspective as a contagious disease. There are many factors associated with obesity, including genetics, low levels of physical activity, and high caloric intake. An earlier study examined the effects of different social factors on an individual’s risk of being obese; it found that people with obese friends and family were at an increased risk for obesity, and this trend was influenced by how close the relationships were.

In this model of the prevalence of obesity, the researchers included a factor to represent obesity as a “social contagion”, reflecting those previous findings and indicating a potential increased risk and increased prevalence due to transmission from one person to another. This mechanism is assumed to be related to people adopting the behaviors of those close to them; notably, activity levels and type and quantity of food consumed. The model predicts obesity rates in populations with terms associated with the genetic contribution to obesity, the mother’s non-genetic contribution to her offspring, and the prevalence of obesity. Essentially, the more obese individuals there are in a society, the more likely it is for someone to know and interact with an obese person.

The models indicate that obesity prevalence plateaus around 35-40% without an intervention. The model is still fairly primitive, but the researchers hope that in future it could provide insight into the effects of potential interventions. For example, is it better to target an intervention to individuals who are already obese, or should the reach of the intervention be more broad and target the population as a whole? When the models reach a level of complexity comparable to the existing factors for obesity, they can be a powerful tool in preventing and addressing the epidemic. (Kelly Servick, Science Magazine)

A study recently published in Nature showed that alterations in brain development in children who go on to be diagnosed with autism precede behavioral symptoms. High-risk infants’ brains were scanned with MRI at 6, 12, and 24 months. It was determined that the infants who were subsequently diagnosed with autism had a faster rate of brain volume growth between 12 and 24 months. Additionally, between 6 and 12 months, these infants had a faster rate of growth in the surface area of folds on the brain, called the cortical surface.

Taking these findings, the research team used a machine learning approach called a deep-learning neural network to make a model to predict whether an infant would be diagnosed with autism based on their MRIs from 6 and 12 months. This model was tested in a larger set of infants, and the model correctly predicted 30 out of 37 infants who went on to be diagnosed (true positives), and it incorrectly predicted that 4 infants would be diagnosed with autism out of the 142 who were not later diagnosed (false positives). These results are much more robust than behavior-based predictions from this same age range.

More work needs to be done to replicate the results in a larger sample. Additionally, all of the participants were high-risk infants, meaning they had a sibling who was diagnosed with autism, so the results are not necessarily generalizable to the rest of the population. Further studies need to be done in the general population to determine if these same patterns are observable, but that would require an even larger sample due to the lower risk. However, the early detection of symptoms and prediction of diagnosis are potentially valuable tools, especially considering another recent publication showed that early intervention in children with autism affects the severity of symptoms years down the road. (Ewen Callaway, Nature News)

Funding totals from 2015 reveal a trending decrease in funding for neglected diseases, excluding Ebola and other viral hemorrhagic fevers. Neglected diseases are diseases that primarily affect developing companies, thus providing little incentive for private research and development by commercial entities; the other diseases include malaria, tuberculosis, and HIV/AIDS. Given the recent surge of funding for Ebola research, the analysis firm, Policy Cures Research, decided to separate it from the other neglected diseases in its analysis to observe funding patterns independent from the epidemic that dominated the news and international concerns. Funding was tracked from private, public, and philanthropic sources.

The funding for Ebola research has primarily gone to development of a vaccine, and over a third of the funds were provided by industry. For the other diseases, the decline in overall funding is mostly represented by a decline in funding from public entities, primarily comprised of the governments of large, developed countries. Those countries accounted for 97% of the research funding for neglected diseases in 2015, so any significant change in that funding category would affect the overall funding amounts. However, there was also a slight decline in philanthropic funding. When including Ebola with the others, funding of neglected diseases was actually at its highest in the past ten years. It is not known whether money was funneled from the other diseases to Ebola research, or if this decline is indicative of less research spending in general. (Erin Ross, Nature News)